T-Drive: Driving Directions Based on Taxi Trajectories
GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.
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This video showcases three application scenarios that have been enabled in the urban computing project. 1) Finding smart driving direction based on taxi trajectories; 2) A passenger-cabbie recommender system; 3) Glean the flawed urban planning in terms of people's city-wide mobility patterns learned from taxi trajectories. Contact: Yu Zheng, Researcher at Microsoft Research Asia, email@example.com [video width="854" height="480" mp4="https://www.microsoft.com/en-us/research/wp-content/uploads/2011/10/urban_planning_Ubicomp2011_yuzheng-2.mp4"][/video]